Agent-based simulations are expected to enable analysis of complex social phenomena. In such simulations, one of the important behaviors of the agents is negotiation. Throughout the negotiations, the agents can make complex interactions with each other. Therefore, the ability of agents to perform negotiation is important in simulations of artificial societies. In this paper, we focus on price negotiations, in which the two sides have opposing interests. In the conventional price negotiation model, the process consists of an alternate succession of directly presented offers and counter-offers exchanging the desired prices. As an extended price negotiation model, we introduce virtual words to mimic the negotiation techniques of humans for indirectly presenting the desired price. The process of the proposed negotiation model consists of an alternate succession of offers of desired price and counter-offers of a word. The words represent the degree of the agent's demand. We propose agents with reinforcement learning who can acquire the ability to distinguish words and use them to negotiate. As a result, we will show that the virtual words became meaningful in the process of negotiations between agents whose negotiating strategies are acquired by reinforcement leaning.

DNA Sequence Design Problem is a crucial problem in information-based biotechnology such as DNA computing. In this paper, we introduce a powerful design strategy for DNA sequences by refining Random Generator. Random Generator is one of the design strategies and offers great advantages, but it is not a good algorithm for generating a large set of DNA sequences. We propose a Two-Step Search algorithm, then show that TSS can generate a larger set of DNA sequences than Random Generator by computer simulation.

Recently, researchers in various fields have shown interest in the behavior of creatures from the viewpoint of adaptiveness and flexibility. Ants, known as social insects, exhibit collective behavior in performing tasks that can not be carried out by an individual ant. In ant colonies, chemical substances, called pheromones, are used as a way to communicate important information on global behavior. For example, ants looking for food lay the way back to their nest with a specific type of pheromone. Other ants can follow the pheromone trail and find their way to baits efficiently. In 1991, Colorni et al. proposed the ant algorithm for Traveling Salesman Problems (TSPs) by using the analogy of such foraging behavior and pheromone communication. In the ant algorithm, there is a colony consisting of many simple ant agents that continuously visit TSP cities with opinions to prefer subtours connecting near cities and they lay strong pheromones. The ants completing their tours lay pheromones of various intensities with passed subtours according to distances. Namely, subtours in TSP tourns that have the possibility of being better tend to have strong pheromones, so the ant agents specify good regions in the search space by using this positive feedback mechanism. In this paper, we propose a multiple ant colonies algorithm that has been extended from the ant algorithm. This algorithm has several ant colonies for solving a TSP, while the original has only a single ant colony. Moreover, two kinds of pheromone effects, positive and negative pheromone effects, are introduced as the colony-level interactions. As a result of colony-level interactions, the colonies can exchange good schemata for solving a problem and can maintain their own variation in the search process. The proposed algorithm shows better performance than the original algorithm with almost the same agent strategy used in both algorithms except for the introduction of colony-level interactions.

In this paper, we propose a new cooperative search algorithm based on pheromone communication for solving the Vehicle Routing Problems. In this algorithm, multi-agents can partition the problem cooperatively and search partial solutions independently using pheromone communication, which mimics the communication method of real ants. Through some computer experiments the cooperative search of multi-agents is confirmed.